Infografía diálogos de saberes

Los diálogos de saberes, también llamados diálogos interculturales, son procesos de comunicación e intercambio entre personas, grupos o comunidades que provienen de diferentes orígenes o culturas. En el caso del sector de la salud, los intercambios se realizan entre determinados grupos o personas y personal de salud capacitado. Su objetivo es, entre otros, mejorar el acceso a los servicios de salud y construir una salud intercultural, con énfasis en la resolución de problemas previamente planteados y sus causas, la comprensión mutua y la creación de vínculos sólidos. Esta infografía destaca los aspectos principales de los Diálogos de saberes

Does Reader Engagement with Neurosurgery Journal Websites Correlate with the Number of Citations Received by Articles?

BACKGROUND: Medicine has begun adapting to new information-sharing paradigms in the hyper-connected social media era. In this milieu, the role of journal websites in the dissemination of clinical and research information needs to be reevaluated. OBJECTIVE: We sought to explore whether reader engagement with neurosurgical journal websites, reflected by the number of article views and downloads, correlated with the eventual number of citations received by the articles. METHODS: The websites of all Medline indexed neurosurgical journals were screened to identify those that provided information regarding the number of abstract and full text views and downloads. Articles published in these journals between July 2010 and June 2011 were included in this analysis. Various article attributes were identified and the number of citations per article was obtained from Google Scholar. The impact factors of the selected journals for the year 2010 were obtained from the Journal Citation Reports. RESULTS: Twenty-two journals that had published 2527 articles were finally included in this analysis. The number of abstract views, full-text views, and downloads all correlated strongly with the journal impact factors in 2010 as well as the eventual citations per article. The number of article downloads independently predicted the citations per article on multivariate analysis. Neurology India had significantly higher article views and downloads but lower citations per article than the other journals. CONCLUSIONS: Readers were found to engage significantly with neurosurgical journal websites and therefore, open access to articles would lead to increased visibility of articles, resulting in higher citation rates.

News and misinformation consumption: A temporal comparison across European countries.

The Internet and social media have transformed the information landscape, democratizing content access and production. While making information easily accessible, these platforms can also act as channels for spreading misinformation, posing crucial societal challenges. To address this, understanding news consumption patterns and unraveling the complexities of the online information environment are essential. Previous studies highlight polarization and misinformation in online discussions, but many focus on specific topics or contexts, often overlooking comprehensive cross-country and cross-topic analyses. However, the dynamics of debates, misinformation prevalence, and the efficacy of countermeasures are intrinsically tied to socio-cultural contexts. This work aims to bridge this gap by exploring information consumption patterns across four European countries over three years. Analyzing the Twitter activity of news outlets in France, Germany, Italy, and the UK, this study seeks to shed light on how topics of European significance resonate across these nations and the role played by misinformation sources. The results spotlight that while reliable sources predominantly shape the information landscape, unreliable content persists across all countries and topics. Though most users favor trustworthy sources, a small percentage predominantly consumes content from questionable sources, with even fewer maintaining a mixed information diet. The cross-country comparison unravels disparities in audience overlap among news sources, the prevalence of misinformation, and the proportion of users relying on questionable sources. Such distinctions surface not only across countries but also within various topics. These insights underscore the pressing need for tailored studies, crucial in designing targeted and effective countermeasures against misinformation and extreme polarization in the digital space.

Workflow for detecting biomedical articles with underlying open and restricted-access datasets.

To monitor the sharing of research data through repositories is increasingly of interest to institutions and funders, as well as from a meta-research perspective. Automated screening tools exist, but they are based on either narrow or vague definitions of open data. Where manual validation has been performed, it was based on a small article sample. At our biomedical research institution, we developed detailed criteria for such a screening, as well as a workflow which combines an automated and a manual step, and considers both fully open and restricted-access data. We use the results for an internal incentivization scheme, as well as for a monitoring in a dashboard. Here, we describe in detail our screening procedure and its validation, based on automated screening of 11035 biomedical research articles, of which 1381 articles with potential data sharing were subsequently screened manually. The screening results were highly reliable, as witnessed by inter-rater reliability values of ≥0.8 (Krippendorff's alpha) in two different validation samples. We also report the results of the screening, both for our institution and an independent sample from a meta-research study. In the largest of the three samples, the 2021 institutional sample, underlying data had been openly shared for 7.8% of research articles. For an additional 1.0% of articles, restricted-access data had been shared, resulting in 8.3% of articles overall having open and/or restricted-access data. The extraction workflow is then discussed with regard to its applicability in different contexts, limitations, possible variations, and future developments. In summary, we present a comprehensive, validated, semi-automated workflow for the detection of shared research data underlying biomedical article publications.

Sharing insect data through GBIF: novel monitoring methods, opportunities and standards.

Technological advancements in biological monitoring have facilitated the study of insect communities at unprecedented spatial scales. The progress allows more comprehensive coverage of the diversity within a given area while minimizing disturbance and reducing the need for extensive human labour. Compared with traditional methods, these novel technologies offer the opportunity to examine biological patterns that were previously beyond our reach. However, to address the pressing scientific inquiries of the future, data must be easily accessible, interoperable and reusable for the global research community. Biodiversity information standards and platforms provide the necessary infrastructure to standardize and share biodiversity data. This paper explores the possibilities and prerequisites of publishing insect data obtained through novel monitoring methods through GBIF, the most comprehensive global biodiversity data infrastructure. We describe the essential components of metadata standards and existing data standards for occurrence data on insects, including data extensions. By addressing the current opportunities, limitations, and future development of GBIF's publishing framework, we hope to encourage researchers to both share data and contribute to the further development of biodiversity data standards and publishing models. Wider commitments to open data initiatives will promote data interoperability and support cross-disciplinary scientific research and key policy indicators. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Identifying strategies to support implementation of interprofessional primary care teams in Nova Scotia: Results of a survey and knowledge sharing event.

BACKGROUND: Interprofessional primary care teams (IPCTs) work together to enhance care. Despite evidence on the benefits of IPCTs, implementation remains challenging. This research aims to 1) identify and prioritize barriers and enablers, and 2) co-develop team-level strategies to support IPCT implementation in Nova Scotia, Canada. METHODS: Healthcare providers and staff of IPCTs were invited to complete an online survey to identify barriers and enablers, and the degree to which each item impacted the functioning of their team. Top ranked items were identified using the sum of frequency x impact for each response. A virtual knowledge sharing event was held to identify strategies to address local barriers and enablers that impact team functioning. RESULTS: IPCT members (n = 117), with a mix of clinic roles and experience, completed the survey. The top three enablers identified were access to technological tools to support their role, standardized processes for using the technological tools, and having a team manager to coordinate collaboration. The top three barriers were limited opportunity for daily team communication, lack of conflict resolution strategies, and lack of capacity building opportunities. IPCT members, administrators, and patients attended the knowledge sharing event (n = 33). Five strategies were identified including: 1) balancing patient needs and provider scope of practice, 2) holding regular and accessible meetings, 3) supporting team development opportunities, 4) supporting professional development, and 5) supporting involvement in non-clinical activities. INTERPRETATION: This research contextualized evidence to further understand local perspectives and experiences of barriers and enablers to the implementation of IPCTs. The knowledge exchange event identified actionable strategies that IPCTs and healthcare administrators can tailor to support teams and care for patients.

Online dissemination of Cochrane reviews on digital health technologies: a cross-sectional study.

BACKGROUND: This cross-sectional study investigated the online dissemination of Cochrane reviews on digital health technologies. METHODS: We searched the Cochrane Database of Systematic Reviews from inception up to May 2023. Cochrane reviews with any population (P), intervention or concept supported by any digital technology (I), any or no comparison (C), and any health outcome (O) were included. Data on review characteristics (bibliographic information, PICO, and evidence quality) and dissemination strategies were extracted and processed. Dissemination was assessed using review information on the Cochrane website and Altmetric data that trace the mentions of academic publications in nonacademic online channels. Data were analysed using descriptive statistics and binary logistic regression analysis. RESULTS: Out of 170 records identified in the search, 100 Cochrane reviews, published between 2005 and 2023, were included. The reviews focused on consumers (e.g. patients, n = 86), people of any age (n = 44), and clinical populations (n = 68). All reviews addressed interventions or concepts supported by digital technologies with any devices (n = 73), mobile devices (n = 17), or computers (n = 10). The outcomes focused on disease treatment (n = 56), health promotion and disease prevention (n = 27), or management of care delivery (n = 17). All reviews included 1-132 studies, and half included 1-10 studies. Meta-analysis was performed in 69 reviews, and certainty of evidence was rated as high or moderate for at least one outcome in 46 reviews. In agreement with the Cochrane guidelines, all reviews had a plain language summary (PLS) that was available in 3-14 languages. The reviews were disseminated (i.e. mentioned online) predominantly via X/Twitter (n = 99) and Facebook (n = 69). Overall, 51 reviews were mentioned in up to 25% and 49 reviews in 5% of all research outputs traced by Altmetric data. Dissemination (i.e. higher Altmetric scores) was associated with bibliographic review characteristics (i.e. earlier publication year and PLS available in more languages), but not with evidence quality (i.e. certainty of evidence rating, number of studies, or meta-analysis performed in review). CONCLUSIONS: Online attention towards Cochrane reviews on digital health technologies is high. Dissemination is higher for older reviews and reviews with more PLS translations. Measures are required to improve dissemination of Cochrane reviews based on evidence quality. SYSTEMATIC REVIEW REGISTRATION: The study was prospectively registered at the Open Science Framework ( https://osf.io/mpw8u/ ).

Research and Future Challenges for Disseminating Strengths-Based Nursing and Healthcare in Japan.

Strengths-Based Nursing and Healthcare (SBNH) has garnered attention in the field of psychiatric nursing in Japan, yet its adoption in other nursing sectors remains limited. Japan is currently facing the formidable challenge of a rapidly aging population and growing demand for healthcare and welfare services. To address these issues, a shift from hospital-based care to comprehensive community care is underway, underscoring the importance of nurses in community settings, where focusing on client strengths is essential. Therefore, this paper aims to present research and practical examples to advocate for the broader dissemination of SBNH in Japan.

Dynamic analysis and optimal control of stochastic information cross-dissemination and variation model with random parametric perturbations.

Information dissemination has a significant impact on social development. This paper considers that there are many stochastic factors in the social system, which will result in the phenomena of information cross-dissemination and variation. The dual-system stochastic susceptible-infectious-mutant-recovered model of information cross-dissemination and variation is derived from this problem. Afterward, the existence of the global positive solution is demonstrated, sufficient conditions for the disappearance of information and its stationary distribution are calculated, and the optimal control strategy for the stochastic model is proposed. The numerical simulation supports the results of the theoretical analysis and is compared to the parameter variation of the deterministic model. The results demonstrate that cross-dissemination of information can result in information variation and diffusion. Meanwhile, white noise has a positive effect on information dissemination, which can be improved by adjusting the perturbation parameters.

Using an epidemiological model to explore the interplay between sharing and advertising in viral videos.

How to exploit social networks to make internet content spread rapidly and consistently is an interesting question in marketing management. Although epidemic models have been employed to comprehend the spread dynamics of internet content, such as viral videos, the effects of advertising and individual sharing on information dissemination are difficult to distinguish. This gap forbids us to evaluate the efficiency of marketing strategies. In this paper, we modify a classic mean-field SIR (susceptible-infected-recovered) model, incorporating the influences of sharing and advertising in viral videos. We mathematically analyze the global stability of the system and propose an agent-based modeling approach to evaluate the efficiency of sharing and advertising. We further provide a case study of music videos on YouTube to show the validity of our model.

Nudge-based misinformation interventions are effective in information environments with low misinformation prevalence.

Nudge-based misinformation interventions are presented as cheap and effective ways to reduce the spread of misinformation online. However, despite online information environments typically containing relatively low volumes of misinformation, most studies testing the effectiveness of nudge interventions present equal proportions of true and false information. As the effectiveness of nudges can be highly context-dependent, it is imperative to validate the effectiveness of nudge-based interventions in environments with more realistic proportions of misinformation. The current study (N = 1387) assessed the effectiveness of a combined accuracy and social-norm nudge in simulated social-media environments with varying proportions of misinformation (50%, 20%, and 12.5%) relative to true and non-news-based (i.e., "social") information. The nudge intervention was effective at improving sharing discernment in conditions with lower proportions of misinformation, providing ecologically valid support for the use of nudge-based interventions to counter misinformation propagation on social media.

A resampling-based approach to share reference panels.

For many genome-wide association studies, imputing genotypes from a haplotype reference panel is a necessary step. Over the past 15 years, reference panels have become larger and more diverse, leading to improvements in imputation accuracy. However, the latest generation of reference panels is subject to restrictions on data sharing due to concerns about privacy, limiting their usefulness for genotype imputation. In this context, here we propose RESHAPE, a method that employs a recombination Poisson process on a reference panel to simulate the genomes of hypothetical descendants after multiple generations. This data transformation helps to protect against re-identification threats and preserves data attributes, such as linkage disequilibrium patterns and, to some degree, identity-by-descent sharing, allowing for genotype imputation. Our experiments on gold-standard datasets show that simulated descendants up to eight generations can serve as reference panels without substantially reducing genotype imputation accuracy.

Difference in Rumor Dissemination and Debunking Before and After the Relaxation of COVID-19 Prevention and Control Measures in China: Infodemiology Study.

BACKGROUND: The information epidemic emerged along with the COVID-19 pandemic. While controlling the spread of COVID-19, the secondary harm of epidemic rumors to social order cannot be ignored. OBJECTIVE: The objective of this paper was to understand the characteristics of rumor dissemination before and after the pandemic and the corresponding rumor management and debunking mechanisms. This study aimed to provide a theoretical basis and effective methods for relevant departments to establish a sound mechanism for managing network rumors related to public health emergencies such as COVID-19. METHODS: This study collected data sets of epidemic rumors before and after the relaxation of the epidemic prevention and control measures, focusing on large-scale network rumors. Starting from 3 dimensions of rumor content construction, rumor propagation, and rumor-refuting response, the epidemic rumors were subdivided into 7 categories, namely, involved subjects, communication content, emotional expression, communication channels, communication forms, rumor-refuting subjects, and verification sources. Based on this framework, content coding and statistical analysis of epidemic rumors were carried out. RESULTS: The study found that the rumor information was primarily directed at a clear target audience. The main themes of rumor dissemination were related to the public's immediate interests in the COVID-19 field, with significant differences in emotional expression and mostly negative emotions. Rumors mostly spread through social media interactions, community dissemination, and circle dissemination, with text content as the main form, but they lack factual evidence. The preferences of debunking subjects showed differences, and the frequent occurrence of rumors reflected the unsmooth channels of debunking. The χ2 test of data before and after the pandemic showed that the P value was less than .05, indicating that the difference in rumor content before and after the pandemic had statistical significance. CONCLUSIONS: This study's results showed that the themes of rumors during the pandemic are closely related to the immediate interests of the public, and the emotions of the public accelerate the spread of these rumors, which are mostly disseminated through social networks. Therefore, to more effectively prevent and control the spread of rumors during the pandemic and to enhance the capability to respond to public health crises, relevant authorities should strengthen communication with the public, conduct emotional risk assessments, and establish a joint mechanism for debunking rumors.

Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society.

This paper presents an analysis on information disorder in social media platforms. The study employed methods such as Natural Language Processing, Topic Modeling, and Knowledge Graph building to gain new insights into the phenomenon of fake news and its impact on critical thinking and knowledge management. The analysis focused on four research questions: 1) the distribution of misinformation, disinformation, and malinformation across different platforms; 2) recurring themes in fake news and their visibility; 3) the role of artificial intelligence as an authoritative and/or spreader agent; and 4) strategies for combating information disorder. The role of AI was highlighted, both as a tool for fact-checking and building truthiness identification bots, and as a potential amplifier of false narratives. Strategies proposed for combating information disorder include improving digital literacy skills and promoting critical thinking among social media users.

The Evidence-Based Practice Process Steps 4, 5, and 6: Integration, Evaluation, and Dissemination.

This is the final column in a series of columns that began with the January/February 2021 issue of Neonatal Network, describing the evidence-based practice (EBP) project. The series has taken the reader through sparking the spirit of inquiry, asking a compelling question, and searching and critically appraising the literature. This column will briefly describe the final three steps: step 4, the integration of evidence with clinical expertise and patient/family preferences; step 5, the evaluation of outcomes of practice changes based on evidence; and step 6, the dissemination of the outcomes of the EBP change.

Factors influencing smartwatch use and comfort with health data sharing: a sequential mixed-method study protocol.

INTRODUCTION: Smartwatches have become ubiquitous for tracking health metrics. These data sets hold substantial potential for enhancing healthcare and public health initiatives; it may be used to track chronic health conditions, detect previously undiagnosed health conditions and better understand public health trends. By first understanding the factors influencing one's continuous use of the device, it will be advantageous to assess factors that may influence a person's willingness to share their individual data sets. This study seeks to comprehensively understand the factors influencing the continued use of these devices and people's willingness to share the health data they generate. METHODS AND ANALYSIS: A two-section online survey of smartwatch users over the age of 18 will be conducted (n ≥200). The first section, based on the expectation-confirmation model, will assess factors influencing continued use of smartwatches while the second section will assess willingness to share the health data generated from these devices. Survey data will be analysed descriptively and based on structural equation modelling.Subsequently, six focus groups will be conducted to further understand the issues raised in the survey. Each focus group (n=6) will consist of three smartwatch users: a general practitioner, a public health specialist and an IT specialist. Young smartwatch users (aged 18-44) will be included in three of the focus groups and middle-aged smartwatch users (aged 45-64) will be included in the other three groups. This is to enhance comparison of opinions based on age groups. Data from the focus groups will be analysed using the microinterlocutor approach and an executive summary.After the focus group, participants will complete a brief survey to indicate any changes in their opinions resulting from the discussion. ETHICS AND DISSEMINATION: The results of this study will be disseminated through publication in a peer-reviewed journal, and all associated data will be deposited in a relevant, publicly accessible data repository to ensure transparency and facilitate future research endeavours.This study was approved by the Social Research Ethic Committee (SREC), University College Cork-SREC/SOM/21062023/2.

Identifying and characterizing superspreaders of low-credibility content on Twitter.

The world's digital information ecosystem continues to struggle with the spread of misinformation. Prior work has suggested that users who consistently disseminate a disproportionate amount of low-credibility content-so-called superspreaders-are at the center of this problem. We quantitatively confirm this hypothesis and introduce simple metrics to predict the top superspreaders several months into the future. We then conduct a qualitative review to characterize the most prolific superspreaders and analyze their sharing behaviors. Superspreaders include pundits with large followings, low-credibility media outlets, personal accounts affiliated with those media outlets, and a range of influencers. They are primarily political in nature and use more toxic language than the typical user sharing misinformation. We also find concerning evidence that suggests Twitter may be overlooking prominent superspreaders. We hope this work will further public understanding of bad actors and promote steps to mitigate their negative impacts on healthy digital discourse.

Best practices for government agencies to publish data: lessons from COVID-19.

Without data, knowing how to respond to the COVID-19 pandemic would have been impossible. Data were crucial to understanding how the disease spread and which efforts successfully protected people. Yet, national agencies often did not publish their data in an optimal way, which made responding to the pandemic challenging. Therefore, learning from what went well and what did not for the future is crucial. Drawing on our first-hand experience of republishing COVID-19 data, we identify seven best practices for how to publish data in an optimal way: collect the data that are relevant; make them comparable; clearly document the data; share them frequently and promptly; publish data at a stable location; choose a reusable format; and license others to reuse the data. These best practices are straightforward, inexpensive, and achievable, with some countries already having implemented most of them during the COVID-19 pandemic. More government agencies following these best practices will enable others to access their data and address the world's public health challenges-including the next pandemic.